Disturbing noise—in contrast to a useful sound signal—is sound that is not intended to meet a certain receiver, e.g., a listener's ears. The generation process of noise and disturbing sound signals can generally be divided into three sub-processes. These are the generation of noise by a noise source, the transmission of the noise away from the noise source and the radiation of the noise signal. Suppression of noise may take place directly at the noise source by means of damping, for example. Suppression may also be achieved by inhibiting or damping the transmission and/or radiation of noise. However, in many applications, these efforts do not yield the desired effect of reducing the noise level in a listening room below an acceptable limit. Deficiencies in noise reduction can be observed especially in the bass frequency range. Additionally or alternatively, noise control methods and systems may be employed that eliminate or at least reduce the noise radiated into a listening room by means of destructive interference, i.e., by superposing the noise signal with a compensation signal. Such systems and methods are summarized under the term active noise canceling or active noise control (ANC).
Although it is known that “points of silence” can be achieved in a listening room by superposing a compensation sound signal and the noise signal to be suppressed such that they destructively interfere, a reasonable technical implementation was not feasible before the development of cost-effective, high-performance digital signal processors, which may be used together with an adequate number of suitable sensors and actuators.
Current systems for actively suppressing or reducing the noise level in a listening room (known as “active noise control” or “ANC” systems) generate a compensation sound signal with the same amplitude and frequency components for each noise signal to be suppressed, but with a phase shift of 180° with respect to the noise signal. The compensation sound signal interferes destructively with the noise signal; the noise is thus eliminated or damped at least at certain positions within the listening room. These positions in which a high damping of noise is achieved are often referred to as “sweet spots”.
In the case of a motor vehicle, the term noise covers, among other things, noise generated by mechanical vibrations of the engine or fans and components mechanically coupled to them, noise generated by the wind when driving and noise generated by the tires. Modern motor vehicles may comprise features such as so-called “rear seat entertainment”, which presents high-fidelity audio using a plurality of loudspeakers arranged within the passenger compartment of the motor vehicle. In order to improve the quality of sound reproduction, disturbing noise has to be considered in digital audio processing. Besides this, another goal of active noise control is to facilitate conversations between people sitting in the rear seats and the front seats.
Modern ANC systems depend on digital signal processing and digital filter techniques. A noise sensor (for example, a microphone or non-acoustic sensor) may be employed to obtain an electrical reference signal that represents the disturbing noise signal generated by a noise source. This reference signal is fed to an adaptive filter; the filtered reference signal is then supplied to an acoustic actuator (e.g., a loudspeaker) that generates a compensation sound field in phase opposition to the noise within a defined portion of the listening room (i.e., within the sweet spot), thus eliminating or at least damping the noise within this defined portion of the listening room. The residual noise signal may be measured by means of microphones in or close to each sweet spot. The resulting microphone output signals may be used as error signals, which are fed back to the adaptive filter, where the filter coefficients of the adaptive filter are modified such that a norm (e.g., the power) of the error signals is minimized.
A known digital signal processing method frequently used in adaptive filters is an enhancement of the known least mean squares (LMS) method for minimizing the error signal, or more precisely the power of the error signal. These enhanced LMS methods include, for example, the filtered-x LMS (FXLMS) algorithm (or modified versions thereof) and related methods such as the filtered-error LMS (FELMS) algorithm. A model that represents the acoustic transmission path from the acoustic actuator (i.e., loudspeaker) to the error signal sensor (i.e., microphone) is thereby used to apply the FXLMS (or any related) algorithm. This acoustic transmission path from the loudspeaker to the microphone is usually referred to as the “secondary path” of the ANC system, whereas the acoustic transmission path from the noise source to the microphone is usually referred to as the “primary path” of the ANC system.
In general, ANC systems have multiple inputs (at least one error microphone in each listening position, i.e., sweet spot) and multiple outputs (a plurality of loudspeakers); they are thus referred to as “multi-channel” or “MIMO” (multiple input/multiple output) systems. In the multi-channel case, the secondary paths are represented as a matrix of transfer functions, each representing the transfer behavior of the listening room from one specific loudspeaker to one specific microphone (including the characteristics of the microphone, loudspeaker, amplifier, etc.).
During operation of the ANC system, the transfer characteristics of the secondary paths may be subject to variations. A particular secondary path transfer function may vary due to many different causes: for example, when the number of listeners in the listening room changes, when a person in a listening position moves, when a window is opened, etc. Such variations result in a mismatch between the actual secondary path transfer characteristics and the transfer characteristics in the model used by the aforementioned LMS methods. Such a mismatch may result in stability problems, a reduced damping of the noise and, consequently, smaller sweet spots.